Machine Learning Course

Spring, 1997/8, M.Sc. programme in AI, Information Technology Department, Faculty of Mathematics and Informatics, Sofia University, Lecturer: Zdravko Markov
  1. Introduction - the AI approach to Machine Learning, subfields
  2. Inductive learning
  3. Memory-based (lazy) learning
  4. Unsupervised Learning
  5. Data Mining and Knowledge Discovery
  6. Explanation-based learning

Introduction to Machine Learning

Learning Semantic Networks by Examples

General setting for induction, languages, structuring the hypothesis space (partial orderings, lgg's)

Transforming relational induction into propositional

Version Space Learning

 Decision trees

 Covering Strategies

 Propositional lgg-based approaches

Relational lgg-based approaches

Building specialization graph

Using information-based heuristics - FOIL
Similarity measures and Nearest Neighbour algorithm

 Simple Bayes algorithm
Agglomerative Clustering
Conceptual Clustering (COBWEB)

Explanation-Based Learning